A distributed database system is a collection of autonomous databases that appear as a single logical database to users. It enables data to be stored across multiple physical locations, offering scalability, reliability, and performance benefits. 🌐

Key Concepts

  • Data Sharding
    Splitting data into partitions (shards) across servers.

    Data_Sharding

  • Replication
    Maintaining copies of data in multiple nodes to ensure redundancy and fault tolerance.

    Data_Replication

  • Consistency Protocols
    Ensuring data integrity across distributed nodes (e.g., Paxos, Raft).

    Consistency_Protocols

  • CAP Theorem
    A fundamental trade-off in distributed systems: Consistency, Availability, and Partition tolerance.

    CAP_Theorem

Architecture Types

  1. Centralized vs. Decentralized
    Centralized systems have a single master node, while decentralized systems distribute control.

    Distributed_System_Topology

  2. Hybrid Models
    Combines sharding with replication for balanced performance and reliability.

    Hybrid_Database_Model

  3. Peer-to-Peer Networks
    Nodes act as both clients and servers, sharing data directly.

    P2P_Database_Network

Use Cases & Challenges

  • Use Cases

    • Global-scale applications
    • High-availability requirements
    • Big data processing

  • Challenges

    • Network latency
    • Data consistency management
    • Security in distributed environments

For deeper insights into why distributed databases are critical for modern applications, visit our Why Distributed Databases? guide. 📘